Exploration of COVID-19 tracking data from multiple resources
Wei Sun
2020-03-30
1 Introduction
Coronavirus disease 2019 (COVID-19) is an infectious disease caused by a new type of coronavirus: severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The outbreak first started in Wuhan, China in December 2019. The first kown case of COVID-19 in the U.S. was confirmed on January 20, 2020, in a 35-year-old man who teturned to Washington State on January 15 after traveling to Wuhan. Starting around the end of Feburary, evidence emerge for community spread in the US.
We, as all of us, are indebted to the heros who fight COVID-19 across the whole world in different ways. For this data exploration, I am grateful to many data science groups who have collected detailed COVID-19 outbreak data, including the number of tests, confirmed cases, and deaths, across countries/regions, states/provnices (administrative division level 1, or admin1), and counties (admin2). Specifically, I used the data from these three resources:
JHU (https://coronavirus.jhu.edu/)
The Center for Systems Science and Engineering (CSSE) at John Hopkins University.
World-wide counts of coronavirus cases, deaths, and recovered ones.
NY Times (https://www.nytimes.com/interactive/2020/us/coronavirus-us-cases.html)
The New York Times
``cumulative counts of coronavirus cases in the United States, at the state and county level, over time’’
COVID Trackng (https://covidtracking.com/)
COVID Tracking Project
``collects information from 50 US states, the District of Columbia, and 5 other US territories to provide the most comprehensive testing data’’
2 JHU
Assume you have cloned the JHU Github repository on your local machine at ``../COVID-19’’.
2.1 time series data
The time series provide counts (e.g., confirmed cases, deaths) starting from Jan 22nd, 2020 for 253 locations. Currently there is no data of individual US state in these time series data files.
Here is the list of 10 records with the largest number of cases or deaths on the most recent date.
Next, I check for each country/region, what is the number of new cases/deaths? This data is important to understand what is the trend under different situations, e.g., population density, social distance policies etc. Here I checked the top 10 countries/regions with the highest number of deaths.
2.2 daily reports data
The raw data from Hopkins are in the format of daily reports with one file per day. More recent files (since March 22nd) inlcude information from individual states of US or individual counties, as shown in the following figure. So I turn to NY Times data for informatoin of individual states or counties.
## [1] 69## [1] "../COVID-19/csse_covid_19_data/csse_covid_19_daily_reports/01-22-2020.csv"
## [2] "../COVID-19/csse_covid_19_data/csse_covid_19_daily_reports/01-23-2020.csv"
## [3] "../COVID-19/csse_covid_19_data/csse_covid_19_daily_reports/03-30-2020.csv"## $title
## [1] "number of records in Hopkins daily reports"
##
## attr(,"class")
## [1] "labels"
3 NY Times
The data from NY Times are saved in two text files, one for state level information and the other one for county level information.
## [1] 19713 6## date county state fips cases deaths
## 1 2020-01-21 Snohomish Washington 53061 1 0
## 2 2020-01-22 Snohomish Washington 53061 1 0## [1] 1499 5## date state fips cases deaths
## 1 2020-01-21 Washington 53 1 0
## 2 2020-01-22 Washington 53 1 0The currente date is
## [1] "2020-03-29"3.1 state level data
First check the 10 states with the largest number of deaths.
## date state fips cases deaths
## 1478 2020-03-29 New York 36 59568 965
## 1496 2020-03-29 Washington 53 4896 207
## 1476 2020-03-29 New Jersey 34 13386 161
## 1464 2020-03-29 Louisiana 22 3540 152
## 1468 2020-03-29 Michigan 26 5486 132
## 1449 2020-03-29 California 6 6266 130
## 1455 2020-03-29 Georgia 13 2683 83
## 1459 2020-03-29 Illinois 17 4613 70
## 1454 2020-03-29 Florida 12 4942 59
## 1467 2020-03-29 Massachusetts 25 4955 48For these 10 states, I check the number of new cases and the number of new deaths. Part of the reason for such checking is to identify whether there is any similarity on such patterns. For example, could you use the pattern seen from Italy to predict what happen in an individual state, and what are the similarities and differences across states.
Next I check the relation between the cumulative number of cases and deaths for these 10 states, starting on March
3.2 county level data
First check the 10 counties with the largest number of deaths.
## date county state fips cases deaths
## 18902 2020-03-29 New York City New York NA 33768 776
## 19611 2020-03-29 King Washington 53033 2163 146
## 18465 2020-03-29 Orleans Louisiana 22071 1350 73
## 18600 2020-03-29 Wayne Michigan 26163 2704 56
## 18166 2020-03-29 Cook Illinois 17031 3445 40
## 18920 2020-03-29 Suffolk New York 36103 5023 40
## 18901 2020-03-29 Nassau New York 36059 6445 39
## 17870 2020-03-29 Los Angeles California 6037 2136 37
## 18836 2020-03-29 Bergen New Jersey 34003 2169 35
## 18585 2020-03-29 Oakland Michigan 26125 1170 34For these 10 counties, I check the number of new cases and the number of new deaths.
4 COVID Trackng
The positive rates of testing can be an indicator on how much the COVID-19 has spread. However, they are more noisy data since the negative testing resutls are often not reported and the tests are almost surely taken on a non-representative random sample of the population. The COVID traking project proides a grade per state: ``If you are calculating positive rates, it should only be with states that have an A grade. And be careful going back in time because almost all the states have changed their level of reporting at different times.’’ (https://covidtracking.com/about-tracker/). The data are also availalbe for both counties and states, here I only look at state level data.
Since the daily postive rate can fluctuate a lot, here I only illustrae the cumulative positave rate across time, for four states with grade A data. Of course since this is an R markdown file, you can modify the source code and check for other states.
5 Session information
## R version 3.6.2 (2019-12-12)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Catalina 10.15.4
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] httr_1.4.1 ggpubr_0.2.5 magrittr_1.5 ggplot2_3.2.1
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.3 pillar_1.4.3 compiler_3.6.2 tools_3.6.2
## [5] digest_0.6.23 evaluate_0.14 lifecycle_0.1.0 tibble_2.1.3
## [9] gtable_0.3.0 pkgconfig_2.0.3 rlang_0.4.4 yaml_2.2.1
## [13] xfun_0.12 gridExtra_2.3 withr_2.1.2 dplyr_0.8.4
## [17] stringr_1.4.0 knitr_1.28 grid_3.6.2 tidyselect_1.0.0
## [21] cowplot_1.0.0 glue_1.3.1 R6_2.4.1 rmarkdown_2.1
## [25] purrr_0.3.3 farver_2.0.3 scales_1.1.0 htmltools_0.4.0
## [29] assertthat_0.2.1 colorspace_1.4-1 ggsignif_0.6.0 labeling_0.3
## [33] stringi_1.4.5 lazyeval_0.2.2 munsell_0.5.0 crayon_1.3.4